{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70292fb7-04e5-4b98-b240-030915e18869",
   "metadata": {},
   "source": [
    "# Graph-PRefLexOR Sample Training Script\n",
    "\n",
    "```bibtext\n",
    "@article{buehler2024PRefLexOR,\n",
    "      title={PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking}, \n",
    "      author={Markus J. Buehler},\n",
    "      year={2024},\n",
    "      eprint={2410.12375},\n",
    "      archivePrefix={arXiv},\n",
    "      primaryClass={cs.AI},\n",
    "      url={https://arxiv.org/abs/2410.12375}, \n",
    "}\n",
    "@misc{buehler2025insitugraphreasoningknowledge,\n",
    "      title={In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR}, \n",
    "      author={Markus J. Buehler},\n",
    "      year={2025},\n",
    "      eprint={2501.08120},\n",
    "      archivePrefix={arXiv},\n",
    "      primaryClass={cs.AI},\n",
    "      url={https://arxiv.org/abs/2501.08120}, \n",
    "}\n",
    "```\n",
    "\n",
    "This script shows an example how the two training phases I and II are implemented"
   ]
  },
  {
   "attachments": {
    "47723f32-1b44-45de-8057-7b1c2a99fada.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "ea1a76fa-e3bb-46cf-aecc-5ba2cbef1519",
   "metadata": {},
   "source": [
    "### Graph-PReFLexOR: In-situ graph reasoning \n",
    "\n",
    "Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning) is a framework that combines graph reasoning with symbolic abstraction to dynamically expand domain knowledge. Inspired by reinforcement learning, Graph-PReFLexOR defines reasoning as a structured mapping, where tasks yield knowledge graphs, abstract patterns, and ultimately, final answers. Inspired by category theory, it encodes concepts as nodes and their relationships as edges, supporting hierarchical inference and adaptive learning through isomorphic representations. \n",
    "\n",
    "![image.png](attachment:47723f32-1b44-45de-8057-7b1c2a99fada.png)"
   ]
  },
  {
   "attachments": {
    "019fd573-4b5b-4076-8dde-679d30cb9121.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "b21655de-1c08-4ec2-b69e-9f34832a5692",
   "metadata": {},
   "source": [
    "### Training phases I and II (implemented here)\n",
    "\n",
    "![image.png](attachment:019fd573-4b5b-4076-8dde-679d30cb9121.png) "
   ]
  },
  {
   "cell_type": "raw",
   "id": "fdd414ea-a4dc-4207-aa56-3e2241529c31",
   "metadata": {
    "tags": []
   },
   "source": [
    "!pip install git+https://github.com/lamm-mit/PRefLexOR.git --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a92b8a4d-1631-4d48-9495-6a7403795ad1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "import openai\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import datasets\n",
    "import multiprocessing\n",
    "from accelerate import PartialState\n",
    "from trl import ModelConfig, DPOConfig, DPOTrainer, ORPOConfig\n",
    "from transformers import (\n",
    "    TrainingArguments,\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    PreTrainedModel,\n",
    "    PreTrainedTokenizerBase,\n",
    "    Trainer,\n",
    "    TrainerCallback,\n",
    "    AutoConfig,\n",
    "    BitsAndBytesConfig,\n",
    "    DataCollator,\n",
    "    is_wandb_available\n",
    ")\n",
    "import torch\n",
    "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model\n",
    "from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n",
    "import inspect\n",
    "import random\n",
    "import warnings\n",
    "from collections import defaultdict\n",
    "from contextlib import contextmanager, nullcontext\n",
    "from copy import deepcopy\n",
    "from functools import partial, wraps\n",
    "\n",
    "from llama_index.core import (\n",
    "    VectorStoreIndex, Document, SimpleDirectoryReader, Settings\n",
    ")\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.extractors import TitleExtractor\n",
    "from llama_index.core.ingestion import IngestionPipeline, IngestionCache\n",
    "from llama_index.core.indices.vector_store.retrievers import VectorIndexRetriever\n",
    "\n",
    "from datasets import load_dataset, concatenate_datasets, Dataset\n",
    "\n",
    "# Custom imports\n",
    "from utils import *\n",
    "from active_trainer import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1ef9537-26a2-4093-9f1f-f13b563a8618",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set HF token if not already set\n",
    "token = 'hf_.....'\n",
    "from huggingface_hub import login\n",
    "login(token=token)\n",
    "\n",
    "#If you want to use OpenAI as inference engine (e.g. in Llama Index, or elsewhere), set these secrets\n",
    "'''\n",
    "openai_api_key = \"sk-.....\"\n",
    "openai.api_key=openai_api_key\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5adf1720-9d40-4fad-8bcd-f947b40e0296",
   "metadata": {},
   "outputs": [],
   "source": [
    "think_start = '<|thinking|>'\n",
    "think_end = '<|/thinking|>'\n",
    "\n",
    "#raw dataset used, data to be expected in 'text' field\n",
    "raw_data= \"lamm-mit/....\"\n",
    "\n",
    "#whether or not to use LoRA to create a trainable model \n",
    "use_LoRA =True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e01145-c084-4dd8-a91e-14f4a589101d",
   "metadata": {},
   "source": [
    "### Start server to serve LLM, e.g. vLLM or Mistral.RS"
   ]
  },
  {
   "cell_type": "raw",
   "id": "25e16641-c332-4b1e-a6a7-3d4c79e1564e",
   "metadata": {},
   "source": [
    "# MistralRS\n",
    "https://github.com/EricLBuehler/mistral.rs\n",
    "\n",
    "~/mistral.rs/target/release/mistralrs-server --port 8000 --isq Q5_1 --no-paged-attn --prefix-cache-n 0  plain -m meta-llama/Llama-3.1-8B-Instruct -a llama\n",
    "\n",
    "# vLLM\n",
    "https://github.com/vllm-project/vllm\n",
    "\n",
    "vllm serve --port 8000  --gpu-memory-utilization 0.3 --max_model_len 30000 --quantization bitsandbytes --load_format bitsandbytes  meta-llama/Llama-3.1-8B-Instruct"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764e9ec0-1e1b-4d16-96be-77350298b854",
   "metadata": {},
   "source": [
    "### Additional helper functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daf4e4f6-09ef-4254-962c-951b2bae8f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(row, manual = True):\n",
    "    \n",
    "    if manual:\n",
    "        row[\"prompt\"] =  f'<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n\\n{row[\"prompt\"]}<|eot_id|>'\n",
    "        row[\"chosen\"] =  f'<|start_header_id|>assistant<|end_header_id|>\\n\\n{row[\"chosen\"]}<|eot_id|>'\n",
    "        row[\"rejected\"] =f'<|start_header_id|>assistant<|end_header_id|>\\n\\n{row[\"rejected\"]}<|eot_id|>'\n",
    "    else:\n",
    "        row[\"prompt\"] = tokenizer.apply_chat_template   ([ {\"role\": \"user\", \"content\":row[\"prompt\"]}],tokenize=False,add_generation_prompt=False )\n",
    "        row[\"chosen\"] = tokenizer.apply_chat_template   ([ {\"role\": \"assistant\", \"content\":row[\"chosen\"]}],tokenize=False,add_generation_prompt=False )\n",
    "        row[\"rejected\"] = tokenizer.apply_chat_template ( [ {\"role\": \"assistant\", \"content\":row[\"rejected\"]}],tokenize=False,add_generation_prompt=False )\n",
    " \n",
    "    return {'prompt': row[\"prompt\"], 'chosen': row[\"chosen\"], 'rejected': row[\"rejected\"], }\n",
    "\n",
    "\n",
    "def generate_dataset(generate_GPT, index, topics=None, only_include_wrong_answers=False, #if set True, we'll only include wrong answers. \n",
    "                     n_questions_for_each=1,  #how many questions for each topic\n",
    "                     number_nodes_to_get=2,   #how many nodes to make Q-A pair\n",
    "                     \n",
    "                     verbatim=False,get_rejected_from_trained_model=True,model=None, tokenizer=None,\n",
    "                     nodes=None, text=None, process=None):\n",
    "    data = {\"prompt\": [], \"chosen\": [], \"rejected\": [], \"rejected_correct\": []}\n",
    "\n",
    "    if isinstance(topics, list): #topics provided as list of strings\n",
    "        for topic in tqdm(topics):\n",
    "            for _ in range(n_questions_for_each):\n",
    "                try:\n",
    "                    question, correct_response, response_trained_model = get_question_and_answers(generate=generate_GPT, \n",
    "                                                        index = index,\n",
    "                                                        topic=topic, get_rejected_from_trained_model=get_rejected_from_trained_model,\n",
    "                                                        number_nodes_to_get=number_nodes_to_get,  \n",
    "                                                        model=model, tokenizer=tokenizer,\n",
    "                                                    )\n",
    "    \n",
    "                    correct_answer=None\n",
    "                    \n",
    "                    if verbatim:\n",
    "                        print (\"-\"*64)\n",
    "                        print (\">Prompt: \", question)\n",
    "                        print (\">Correct response: \", correct_response)\n",
    "                        print (\">Response model: \", response_trained_model)\n",
    "                        print (\">Correct? \", correct_answer)\n",
    "                    if only_include_wrong_answers:\n",
    "                        correct_answer =False #is_answer_correct(generate_GPT, correct_response, response_trained_model)\n",
    "                    \n",
    "                        if correct_answer==False:\n",
    "                            #do not add if answer is correct\n",
    "                            data[\"prompt\"].append(question)\n",
    "                            data[\"chosen\"].append(correct_response)\n",
    "                            data[\"rejected\"].append(response_trained_model)\n",
    "                            data[\"rejected_correct\"].append (correct_answer)\n",
    "                    else:\n",
    "                        data[\"prompt\"].append(question)\n",
    "                        data[\"chosen\"].append(correct_response)\n",
    "                        data[\"rejected\"].append(response_trained_model)\n",
    "                        data[\"rejected_correct\"].append (correct_answer)\n",
    "                except Exception as e:\n",
    "                    print(f\"An error occurred: {e}\")\n",
    "                    \n",
    "    else: #no topics provided, use random nodes\n",
    "        for _ in tqdm(range(topics)):\n",
    "            for _ in range(n_questions_for_each):\n",
    "                try:\n",
    "                    question, correct_response, response_trained_model = get_question_and_answers(generate=generate_GPT, \n",
    "                                                          index = index, get_rejected_from_trained_model=get_rejected_from_trained_model,\n",
    "                                                          number_nodes_to_get=number_nodes_to_get,  \n",
    "                                                          model=model, tokenizer=tokenizer,\n",
    "                                                    )\n",
    "        \n",
    "                    correct_answer=None\n",
    "                    if verbatim:\n",
    "                        print (\"-\"*64)\n",
    "                        print (\">Prompt: \", question)\n",
    "                        print (\">Correct response: \", correct_response)\n",
    "                        print (\">Response model: \", response_trained_model)\n",
    "                        print (\">Correct? \", correct_answer)\n",
    "                    if only_include_wrong_answers==False:\n",
    "                        \n",
    "                        correct_answer=False#is_answer_correct(generate_GPT, correct_response, response_trained_model)\n",
    "                        if correct_answer==False:\n",
    "                            #here do not add if answer is correct\n",
    "                            data[\"prompt\"].append(question)\n",
    "                            data[\"chosen\"].append(correct_response)\n",
    "                            data[\"rejected\"].append(response_trained_model)\n",
    "                            data[\"rejected_correct\"].append (correct_answer)\n",
    "                    else:\n",
    "                        data[\"prompt\"].append(question)\n",
    "                        data[\"chosen\"].append(correct_response)\n",
    "                        data[\"rejected\"].append(response_trained_model)\n",
    "                        data[\"rejected_correct\"].append (correct_answer)\n",
    "               \n",
    "                except Exception as e:\n",
    "                    print(f\"An error occurred: {e}\")                    \n",
    "    \n",
    "    hf_dataset = datasets.Dataset.from_dict(data)\n",
    "    \n",
    "    if process!=None:\n",
    "        hf_dataset = hf_dataset.map(\n",
    "            process,\n",
    "            num_proc=multiprocessing.cpu_count(),\n",
    "        )\n",
    "        \n",
    "    return hf_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1dba64-57b7-4008-b965-6f697f0b4d7f",
   "metadata": {},
   "source": [
    "### Set up Llama Index for RAG, embeddings, and other tools "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3767786e-07b2-4c06-b6fe-cce0b9bd4046",
   "metadata": {},
   "outputs": [],
   "source": [
    "Settings.embed_model = HuggingFaceEmbedding(\n",
    "    model_name=\"BAAI/bge-large-en-v1.5\" #or other embedding models\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd59092f-630e-4393-90fa-8d6f115cdebb",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load your dataset\n",
    "dataset = datasets.load_dataset(raw_data)['train']\n",
    "documents = [Document(text=dataset[i]['text']) for i in range (len (dataset))]\n",
    "\n",
    "pipeline = IngestionPipeline(\n",
    "    transformations=[\n",
    "        SentenceSplitter(chunk_size=1024, chunk_overlap=20),\n",
    "    ],\n",
    "   )\n",
    "\n",
    "nodes = pipeline.run(documents=documents,  show_progress=True,)\n",
    "index = VectorStoreIndex(nodes, show_progress=True,)\n",
    "retriever = index.as_retriever()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3545050f-36d3-4fa9-9c0a-621d92119486",
   "metadata": {},
   "source": [
    "### Load base model and create trainable version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39550e8f-0f9b-418c-b01c-cf780670d66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "device='cuda:0'\n",
    "\n",
    "model_name='meta-llama/Llama-3.2-3B-Instruct'\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "try:\n",
    "    del model\n",
    "    del tokenizer\n",
    "except:\n",
    "    print ()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "try:\n",
    "    del ref_model\n",
    "     \n",
    "except:\n",
    "    print ()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    trust_remote_code=True,\n",
    "    use_cache=False,\n",
    "     \n",
    "    device_map=\"auto\",\n",
    "    torch_dtype =torch.bfloat16,\n",
    "    attn_implementation=\"flash_attention_2\",\n",
    "    #device_map=\"cuda:0\",\n",
    ").to (device)\n",
    "model.config.use_cache = False\n",
    "\n",
    "model_name_tokenizer='lamm-mit/meta-llama-Meta-Llama-3.2-3B-Instruct-scratchpadtokenizer'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_tokenizer, trust_remote_code=True,\n",
    "                                           use_fast=False,\n",
    "                                         )\n",
    "pad_token='<|finetune_right_pad_id|>'\n",
    "tokenizer.pad_token = pad_token\n",
    "tokenizer.padding_side = \"right\"  \n",
    "\n",
    "if use_LoRA:\n",
    "    from peft import LoraConfig, get_peft_model\n",
    "\n",
    "    lora_alpha = 64\n",
    "    lora_dropout = 0.1\n",
    "    lora_r = 64   \n",
    "    peft_config = LoraConfig(\n",
    "        lora_alpha=lora_alpha,\n",
    "        lora_dropout=lora_dropout,\n",
    "        r=lora_r,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\", #[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"gate_proj\"]\n",
    "        target_modules=[\n",
    "            \"q_proj\",\n",
    "            \"k_proj\",\n",
    "            \"v_proj\",\n",
    "            \"o_proj\",\n",
    "            \"gate_proj\",\n",
    "            \"up_proj\",\n",
    "            \"down_proj\",\n",
    "            \"embed_tokens\", #include embed/lm_head as we are defining a new token\n",
    "            \"lm_head\",\n",
    "        ],\n",
    "    ) \n",
    "    model=get_peft_model(model, peft_config)\n",
    "    \n",
    "    model.print_trainable_parameters()\n",
    "    ref_model=None\n",
    "else:\n",
    "    ref_model=AutoModelForCausalLM.from_pretrained(\n",
    "        model_name,\n",
    "        trust_remote_code=True,\n",
    "        use_cache=False,\n",
    "         \n",
    "        device_map=\"auto\",\n",
    "        torch_dtype =torch.bfloat16,\n",
    "        attn_implementation=\"flash_attention_2\",\n",
    "         \n",
    "    ).to (device)\n",
    "    ref_model.config.use_cache = False\n",
    "\n",
    "tokenizer.encode (f'{think_start}{think_end}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82533cf9-128f-4a4e-b163-8d870b6d902b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Setting up default values for the API key, model, and organization\n",
    "generate_GPT_MistralRS = partial(\n",
    "    generate_OpenAI,\n",
    "    openai_api_key='NONE',   \n",
    "    model='meta-llama/Llama-3.1-8B-Instruct',\n",
    "    base_url=\"http://localhost:8000/v1\"\n",
    "   )\n",
    "\n",
    "prompt='What is spider silk?'\n",
    "messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": 'You are a materials scientist with expertise in biological materials, mechanics, and related fields.',\n",
    "            },\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": prompt,\n",
    "            }\n",
    "        ] \n",
    "\n",
    "res, _ = generate_GPT_MistralRS (messages=messages)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5de82e8-e5f0-4af6-aaa2-ed49fadfd338",
   "metadata": {},
   "source": [
    "### Define functions to generate graph, abstractions and other parts of the dataset on-the-fly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24ed73ed-5b2c-4055-a702-94d1f6afc308",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_knowledge_graph(generate_fn, query_engine, question, context, use_rag=True):\n",
    "    \"\"\"\n",
    "    Generates a knowledge graph with core relationships and a simple abstract pattern layer.\n",
    "    Uses RAG to enhance the abstract understanding.\n",
    "    \"\"\"\n",
    "    # Generate base knowledge graph first\n",
    "    concept_prompt = \"\"\"\n",
    "Create a focused knowledge graph for this question. Include only the most important elements:\n",
    "\n",
    "1. Key Concepts (3-5 main concepts from the question and context)\n",
    "\n",
    "2. Essential Relationships (using only these types):\n",
    "   - IS-A: classification/type relationships\n",
    "   - RELATES-TO: how concepts connect\n",
    "   - INFLUENCES: how one concept affects another\n",
    "\n",
    "Format each relationship as:\n",
    "[Concept A] -[Relationship]-> [Concept B]\n",
    "\n",
    "Question: {question}\n",
    "Context: {context}\n",
    "\"\"\"\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": \"You are a materials science expert. Create a focused knowledge graph with only the most important concepts and relationships.\"},\n",
    "        {\"role\": \"user\", \"content\": concept_prompt.format(question=question, context=context)}\n",
    "    ]\n",
    "    \n",
    "    base_graph, _ = generate_fn(messages=messages)\n",
    "    \n",
    "    # Use RAG to identify abstract patterns\n",
    "    abstract_prompt = f\"\"\"\n",
    "For this question and context, identify:\n",
    "1. The general pattern of relationship between concepts (e.g., A affects B, which causes C)\n",
    "2. The type of change or transformation involved (e.g., increase in X leads to decrease in Y)\n",
    "3. Any key dependencies or conditions\n",
    "\n",
    "Question: {question}\n",
    "Context: {context}\n",
    "\n",
    "Keep the response very brief and focused on the essential pattern.\n",
    "\"\"\"\n",
    "    \n",
    "    abstract_insights = query_engine.query(abstract_prompt)\n",
    "    \n",
    "    # Generate abstract pattern layer\n",
    "    pattern_prompt = f\"\"\"\n",
    "Based on the core concepts and insights, create a simple abstract pattern.\n",
    "Use α, β, γ for abstract concepts and → for relationships.\n",
    "\n",
    "Core Knowledge Graph:\n",
    "{base_graph}\n",
    "\n",
    "Insights:\n",
    "{abstract_insights.response}\n",
    "\n",
    "Create only:\n",
    "1. One abstract pattern showing the general relationship structure\n",
    "2. One key transformation rule\n",
    "3. One essential condition\n",
    "\n",
    "Use simple mathematical symbols (→, ∝, ↑, ↓).\n",
    "\"\"\"\n",
    "    \n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": \"Create a simple abstract pattern from the knowledge graph.\"},\n",
    "        {\"role\": \"user\", \"content\": pattern_prompt}\n",
    "    ]\n",
    "    \n",
    "    abstract_pattern, _ = generate_fn(messages=messages)\n",
    "    \n",
    "    return format_knowledge_graph(base_graph, abstract_pattern, abstract_insights.response)\n",
    "\n",
    "\n",
    "def format_knowledge_graph(base_graph, abstract_pattern, rag_insights):\n",
    "    \"\"\"\n",
    "    Formats the knowledge graph with its abstract pattern layer.\n",
    "    \"\"\"\n",
    "    formatted_graph = \"**Knowledge Graph**\\n\\n\"\n",
    "    \n",
    "    # Core knowledge\n",
    "    formatted_graph += \"Core Concepts and Relationships:\\n\"\n",
    "    formatted_graph += base_graph.strip() + \"\\n\\n\"\n",
    "    \n",
    "    # Abstract pattern\n",
    "    formatted_graph += \"Abstract Pattern:\\n\"\n",
    "    formatted_graph += abstract_pattern.strip() + \"\\n\\n\"\n",
    "    \n",
    "    # RAG insights\n",
    "    formatted_graph += \"Pattern Context:\\n\"\n",
    "    formatted_graph += rag_insights.strip()\n",
    "    \n",
    "    return formatted_graph\n",
    "\n",
    "\n",
    "def get_question_and_answers(\n",
    "    generate,\n",
    "    index,\n",
    "    topic=None,\n",
    "    number_nodes_to_get=2,\n",
    "    nodes=None,\n",
    "    text=None,\n",
    "    get_rejected_from_trained_model=True,\n",
    "    model=None,\n",
    "    tokenizer=None,\n",
    "    use_rag=True,\n",
    "    categories=None,\n",
    "):\n",
    "    # Keep existing categories and add Knowledge Graph\n",
    "    if categories is None:\n",
    "        categories = [\n",
    "            \"Reasoning Steps\",\n",
    "            \"Relevant Materials or Concepts\",\n",
    "            \"Design Principles\",\n",
    "            \"Material Properties\",\n",
    "            \"Hypothesis\",\n",
    "        ]\n",
    "\n",
    "    # Existing context retrieval\n",
    "    context = retrieve_context(index, topic, number_nodes_to_get, nodes, text)\n",
    "    \n",
    "    # Generate the question\n",
    "    question = generate_question(generate, context)\n",
    "    \n",
    "    # Enhance context with RAG if enabled\n",
    "    enriched_context = enhance_context_with_rag(index, question, context) if use_rag else context\n",
    "    \n",
    "    # Generate knowledge graph\n",
    "    \n",
    "    # Create query engine\n",
    "    query_engine = index.as_query_engine()\n",
    "    \n",
    "    # Generate enhanced knowledge graph\n",
    "    knowledge_graph = generate_knowledge_graph(generate, query_engine, question, enriched_context, use_rag)\n",
    "    \n",
    "    # Extract information for each category\n",
    "    extracted_info = extract_categories(generate, categories, question, enriched_context,\n",
    "                                     add_RAG=True)\n",
    "    \n",
    "    # Add knowledge graph to extracted info\n",
    "    extracted_info = OrderedDict([(\"Knowledge Graph\", knowledge_graph), *extracted_info.items()])\n",
    "\n",
    "    # Assemble scratchpad with knowledge graph included\n",
    "    scratchpad = assemble_scratchpad(extracted_info, '')\n",
    "    \n",
    "    # Generate correct and rejected answers\n",
    "    correct_response = generate_correct_answer(generate, question, enriched_context, scratchpad)\n",
    "    correct_response_with_scratchpad = f\"{scratchpad}\\n{correct_response}\"\n",
    "    \n",
    "    rejected_answer = generate_rejected_answer(\n",
    "        generate, question, get_rejected_from_trained_model, model, tokenizer\n",
    "    )\n",
    "    \n",
    "    return question + f' Use {think_start}.', correct_response_with_scratchpad, rejected_answer\n",
    "\n",
    "\n",
    "def retrieve_context(index, topic, number_nodes_to_get, nodes, text):\n",
    "    if nodes is None and text is None:\n",
    "        if topic:\n",
    "            _, concatenated_text = get_nodes_for_topic(index, topic, number_nodes_to_get)\n",
    "        else:\n",
    "            _, concatenated_text = get_random_nodes(index, number_nodes_to_get)\n",
    "    else:\n",
    "        concatenated_text = text or \" \".join([node.text for node in nodes])\n",
    "    token_length=len(tokenizer.encode(concatenated_text))\n",
    "    print (\"Token length of tokenized node data: \", token_length)\n",
    "    return concatenated_text\n",
    "\n",
    "    \n",
    "def get_random_nodes(index, number_nodes_to_get=5):\n",
    "    # Get all nodes from the index\n",
    "    all_nodes = list(index.docstore.docs.values())\n",
    "    \n",
    "    # Ensure we don't request more nodes than exist in the index\n",
    "    number_nodes_to_get = min(number_nodes_to_get, len(all_nodes))\n",
    "    \n",
    "    # Randomly select N nodes\n",
    "    random_nodes = random.sample(all_nodes, number_nodes_to_get)\n",
    "    \n",
    "    # Concatenate their text\n",
    "    concatenated_text = \" \".join([node.text for node in random_nodes])\n",
    "    \n",
    "    return random_nodes, concatenated_text\n",
    "\n",
    "\n",
    "def generate_question(generate_fn, context):\n",
    "    question_gen_query = (\n",
    "        \"You are a Teacher/Professor. Your task is to setup \"\n",
    "        \"a quiz/examination. Using information in the provided context, formulate \"\n",
    "        \"a single question that captures an important fact from the \"\n",
    "        \"context. Restrict the question to the context information provided, \"\n",
    "        \"and make sure this is a question that a highly trained domain expert can answer without seeing the context.\\n\\n\"\n",
    "        \"Just return the question, nothing else. Do not refer to the context, a paper, names, or authors, just ask the question. \"\n",
    "        \"Do not refer to names, persons, or specific text/context. \"\n",
    "        \"The question must be challenging, deep, and stand on its own and query facts and expert domain knowledge. \"\n",
    "        \"The question must NOT refer to a study, or paper, or a specific author.\"\n",
    "    )\n",
    "\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": 'You are a materials scientist.'},\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": f\"{question_gen_query}\\n\\nContext: {context}\",\n",
    "        },\n",
    "    ]\n",
    "    question, _ = generate_fn(messages=messages, temperature=0.7)\n",
    "    return question.strip()\n",
    "\n",
    "\n",
    "def enhance_context_with_rag(index, question, context):\n",
    "    query_engine = index.as_query_engine()\n",
    "    answer = query_engine.query(f\"{question}\\n\\nProvide detailed context and reasoning.\")\n",
    "    enriched_context = f\"{context}\\nAdditional Information: {answer.response}\"\n",
    "    return enriched_context\n",
    "\n",
    "\n",
    "def extract_categories(generate_fn, categories, question, context, index=None, add_RAG=False):\n",
    "    extracted_info = {}\n",
    "    for category in categories:\n",
    "        prompt = f\"\"\"\n",
    "Based on the context, extract the \"{category}\" relevant to the question, keep it brief.\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Context: {context}\n",
    "\n",
    "Provide only the \"{category}\" without additional explanations.\n",
    "\n",
    "If you cannot find any, respond with an empty string. Keep the answer brief, but use step-by-step reasoning and a clear explanation. \n",
    "\n",
    "Just provide the answer, do not refer to the context.\n",
    "\"\"\"\n",
    "        messages = [\n",
    "            {\"role\": \"system\", \"content\": 'You are a helpful assistant who provides well-reasoned, but succinct responses. Act like a professor.'},\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ]\n",
    "        response, _ = generate_fn(messages=messages)\n",
    "        if index != None:\n",
    "            prompt = f\"\"\"\n",
    "Use the draft and improve the \"{category}\" relevant to the question.\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Draft: {response}\n",
    "\n",
    "Provide only the \"{category}\" without additional explanations.\n",
    "\n",
    "If you cannot find any, respond with an empty string. Keep the answer brief, but use step-by-step reasoning and a clear explanation. \n",
    "\n",
    "Just provide the answer, do not refer to the context.\n",
    "\"\"\"\n",
    "            response_RAG = query_engine.query(f\"{prompt}\")\n",
    "            print (\"Improve category with RAG\")\n",
    "            response=response_RAG\n",
    "        extracted_info[category] = response.strip()\n",
    "\n",
    "    if add_RAG:\n",
    "        prompt = f\"\"\"\n",
    "Give additional background relevant for answering the question\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Do not answer the question, just provide additional relevant information and insights. Keep it brief.\n",
    "\"\"\"\n",
    "        extracted_info['Additional Background']  = query_engine.query(f\"{prompt}\").response.strip()\n",
    "        \n",
    "    return extracted_info\n",
    "\n",
    "\n",
    "def assemble_scratchpad(extracted_info, reasoning_steps):\n",
    "    scratchpad = f\"{think_start}\\n\"\n",
    "    #scratchpad += \"Extracted Information:\\n\"\n",
    "    for category, content in extracted_info.items():\n",
    "        scratchpad += f\"**{category}**:\\n\\n{content}\\n\\n\"\n",
    "    #scratchpad += \"\\n**Reasoning Steps:**\\n\\n\"\n",
    "    #scratchpad += reasoning_steps\n",
    "    scratchpad += f\"{think_end}\"\n",
    "    return scratchpad\n",
    "\n",
    "\n",
    "def generate_correct_answer(generate_fn, question, context, thinking):\n",
    "    prompt = f\"\"\"\n",
    "Using the context provided, answer the following question:\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Context: {context}\n",
    "\n",
    "Reasoning: {thinking}\n",
    "\n",
    "Provide a comprehensive and accurate answer.\n",
    "\"\"\"\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": 'You are a knowledgeable materials scientist.'},\n",
    "        {\"role\": \"user\", \"content\": prompt},\n",
    "    ]\n",
    "    answer, _ = generate_fn(messages=messages)\n",
    "    return answer.strip()\n",
    "\n",
    "\n",
    "def generate_rejected_answer(generate_fn, question, get_from_trained_model, model, tokenizer):\n",
    "    if get_from_trained_model:\n",
    "        # Implement logic to generate using the trained model\n",
    "        incorrect_answer, _ = generate_local_model(\n",
    "            model=model,\n",
    "            tokenizer=tokenizer,\n",
    "            prompt=f\"You provide the answer to: {question}\\nThe answer is:\",\n",
    "            system_prompt='You are a materials scientist.',\n",
    "        )\n",
    "    else:\n",
    "        prompt = f\"\"\"\n",
    "You are to provide an incorrect answer to the question below.\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Do not include any reasoning or refer back to the question.\n",
    "\n",
    "Just provide the incorrect answer.\n",
    "\"\"\"\n",
    "        messages = [\n",
    "            {\"role\": \"system\", \"content\": 'You are a materials scientist.'},\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ]\n",
    "        incorrect_answer, _ = generate_fn(messages=messages)\n",
    "    return incorrect_answer.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea420ff6-0f36-4b97-965e-144c7cdcc268",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Example usage of the function\n",
    "question, correct_ans, rejected_ans=get_question_and_answers(generate=generate_GPT_MistralRS, model=model,\n",
    "                                                             tokenizer=tokenizer, index=index, topic=None, \n",
    "                                                              \n",
    "                             number_nodes_to_get=3, nodes=None, text=None, \n",
    "                             get_rejected_from_trained_model=True,\n",
    "                                                            )\n",
    "\n",
    "print(\"Question:\")\n",
    "print(question)\n",
    "print(\"\\nCorrect Answer with THINKING:\")\n",
    "print(correct_ans)\n",
    "print(\"\\nRejected Answer:\")\n",
    "print(rejected_ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0321b119-428b-4180-aeea-11854efc07a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Setting up default values for the API key, model, and organization\n",
    "generate_GPT_local = partial(\n",
    "    generate_local_model,\n",
    "    model=model, tokenizer=tokenizer,\n",
    ")\n",
    "\n",
    "prompt=f'What is spider silk? Use a {think_start}.'\n",
    "messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": 'You are a materials scientist.',\n",
    "            },\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": prompt,\n",
    "            }\n",
    "        ] \n",
    "\n",
    "res, _ = generate_GPT_local (messages=messages,max_new_tokens=128, #prepend_response=f'<|thinking|>',\n",
    "                            )\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53bc3678-4045-42f6-8286-ab400706a159",
   "metadata": {},
   "source": [
    "### Phase I: Structured Thought Integration Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7aef3c9-5086-4d73-a8ca-c33145b6d933",
   "metadata": {},
   "outputs": [],
   "source": [
    "FT_model_name='Graph_PReFLexOR_Phase_I_results'\n",
    "model_current=FT_model_name+'_'\n",
    "\n",
    "#How many topics are generated in each training step\n",
    "topics= 50\n",
    "\n",
    "#How many questions per topic \n",
    "num_questions_per_topic=1\n",
    "\n",
    "#How many epochs trained in each training step\n",
    "num_epochs_per_dataset_generation=3\n",
    "\n",
    "max_prompt_length=512\n",
    "max_length=1024\n",
    "\n",
    "###############################################\n",
    "cfg =  ORPOConfig(\n",
    "    output_dir=FT_model_name,               # usual HF Trainer args: https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer.args\n",
    "    num_train_epochs=1,                     # number of training epochs\n",
    "    per_device_train_batch_size=1,          # batch size per device during training\n",
    "    gradient_accumulation_steps=2,          # number of steps before performing a backward/update pass\n",
    "    gradient_checkpointing=True,            # use gradient checkpointing to save memory\n",
    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
    "    logging_steps=10,                       # log every .. steps\n",
    "    bf16=True,                              # use bfloat16 precision\n",
    "    #tf32=True,                             # use tf32          \n",
    "    learning_rate=5e-5,                     # learning rate\n",
    "    warmup_ratio=0,\n",
    "    warmup_steps=0,\n",
    "    #lr_scheduler_type=\"cosine\",\n",
    "    lr_scheduler_type=\"constant\",\n",
    "    max_prompt_length=max_prompt_length,\n",
    "    remove_unused_columns=False,\n",
    "    max_length=max_length,\n",
    "    beta=0.1,                               # ORPO beta\n",
    "    save_total_limit=3,                     # args related to saving the model...\n",
    "    #save_strategy=\"epoch\",\n",
    "    save_strategy=\"no\",\n",
    "    #push_to_hub=True,                       \n",
    "    hub_private_repo=True,\n",
    "    #report_to=['wandb'],                    # report metrics to Weights & Biases\n",
    "    hub_model_id=f'lamm-mit/{FT_model_name}',\n",
    ")\n",
    "###############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1971cb1f-800b-4fde-970b-cee05dbce23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "if isinstance(topics, list) and all(isinstance(t, str) for t in topics):\n",
    "    n_steps = len(topics) * num_questions_per_topic*num_epochs_per_dataset_generation\n",
    "else:\n",
    "    n_steps = topics * num_questions_per_topic*num_epochs_per_dataset_generation\n",
    "\n",
    "trainer = PRefLexORORPOTrainer(\n",
    "         model=model,\n",
    "         args=cfg,\n",
    "         train_dataset=None,\n",
    "         #train_dataset=temp,\n",
    "         tokenizer=tokenizer,\n",
    "         n_steps=n_steps,  # Train for 50 steps before updating dataset\n",
    "         #topics=topics,\n",
    "         topics=topics,\n",
    "         number_nodes_to_get=3,\n",
    "         n_questions_for_each=num_questions_per_topic,\n",
    "         only_include_wrong_answers=False, \n",
    "         process=process,\n",
    "         generate_dataset=generate_dataset,\n",
    "         generate=generate_GPT_MistralRS,  #generate_GPT_OpenAI,\n",
    "         index=index,\n",
    "         get_rejected_from_trained_model=True,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a70355-2aee-40ab-8fc1-4e1b41307588",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "system_prompt='You are a materials scientist.'\n",
    "\n",
    "# Training loop\n",
    "num_iterations = 50\n",
    "for iteration in range(num_iterations):\n",
    "    print(f\"Starting iteration {iteration + 1}/{num_iterations}\")\n",
    "    \n",
    "    # Train for N steps\n",
    "    trainer.train()\n",
    "    \n",
    "    print (64*\"#\") \n",
    "    txt='Tell me why hierarchical structures work so well.'+f' Use {think_start}.'\n",
    "    #txt='What is the reported work of fracture of the nacre in the abalone shell compared to its mineral constituent?'\n",
    "    output_text, _ =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,   prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text)\n",
    "    print (64*\"-\")\n",
    "    txt=f'What is the relationship between materials and music?'+f' Use {think_start}.'\n",
    "    #txt='What is the reported work of fracture of the nacre in the abalone shell compared to its mineral constituent?'\n",
    "    output_text, messages =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,  prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text)\n",
    "    print (64*\"-\")\n",
    "    txt='Tell me why hierarchical structures work so well.'+f' Use {think_start}.'\n",
    "    #txt='What is the reported work of fracture of the nacre in the abalone shell compared to its mineral constituent?'\n",
    "    output_text, messages =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,  prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text)\n",
    "    print (64*\"#\")\n",
    "    \n",
    "    trainer.update_dataset()\n",
    "    \n",
    "    trainer.save_model(f\"./{model_current}\")\n",
    "    model.push_to_hub (f\"lamm-mit/{model_current}\", private=True)\n",
    "    tokenizer.push_to_hub (f\"lamm-mit/{model_current}\", private=True)\n",
    "\n",
    "    print(f\"Completed iteration {iteration + 1}/{num_iterations}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3164528-9d0e-4e5e-8f71-2956f52e6647",
   "metadata": {},
   "source": [
    "### Phase II: Independent Reasoning Development\n",
    "\n",
    "#### First, merge adapter into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "845d079d-4e91-4b59-b78b-c1fb2c6a6bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "import gc \n",
    "try:\n",
    "    del model\n",
    "    del tokenizer\n",
    "    del merged_model\n",
    "except:\n",
    "    print ()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "try:\n",
    "    del ref_model\n",
    "     \n",
    "except:\n",
    "    print ()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "model_name=\"lamm-mit/meta-llama-Llama-3.2-3B-Instruct-untied\"\n",
    "model_base = AutoModelForCausalLM.from_pretrained(model_name,     torch_dtype =torch.bfloat16,\n",
    "    attn_implementation=\"flash_attention_2\",device_map=\"auto\",trust_remote_code=True,\n",
    "    \n",
    "     )\n",
    " \n",
    "peft_model_id = model_current\n",
    "\n",
    "model_base = PeftModel.from_pretrained(model_base, peft_model_id)\n",
    "\n",
    "model = model_base.merge_and_unload()\n",
    "\n",
    "model_name_tokenizer='lamm-mit/meta-llama-Meta-Llama-3.2-3B-Instruct-scratchpadtokenizer'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_tokenizer, trust_remote_code=True,\n",
    "                                           use_fast=False,\n",
    "                                         )\n",
    "pad_token='<|finetune_right_pad_id|>'\n",
    "tokenizer.pad_token = pad_token\n",
    "tokenizer.padding_side = \"right\"  \n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "try:\n",
    "    del model_base\n",
    "     \n",
    "except:\n",
    "    print ()\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db03a16a-8648-47a8-bb67-b66f86c07237",
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_LoRA:\n",
    "\n",
    "    from peft import LoraConfig, get_peft_model\n",
    "    lora_alpha = 64\n",
    "    lora_dropout = 0.1\n",
    "    lora_r = 64\n",
    "\n",
    "    peft_config = LoraConfig(\n",
    "        lora_alpha=lora_alpha,\n",
    "        lora_dropout=lora_dropout,\n",
    "        r=lora_r,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\", \n",
    "        target_modules=[\n",
    "            \"q_proj\",\n",
    "            \"k_proj\",\n",
    "            \"v_proj\",\n",
    "            \"o_proj\",\n",
    "            \"gate_proj\",\n",
    "            \"up_proj\",\n",
    "            \"down_proj\",\n",
    "            #\"embed_tokens\",\n",
    "            #\"lm_head\",\n",
    "        ], \n",
    "      #  modules_to_save=[\"embed_tokens\", \"lm_head\"]\n",
    "    ) \n",
    "    \n",
    "    from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model\n",
    "    \n",
    "    #model = prepare_model_for_kbit_training(model)\n",
    "    model=get_peft_model(model, peft_config)\n",
    "    \n",
    "    model.print_trainable_parameters()\n",
    "    ref_model=None\n",
    "else:\n",
    "    print (\"We will not use LoRA\")\n",
    "\n",
    "    ref_model=AutoModelForCausalLM.from_pretrained(\n",
    "        model_name,\n",
    "        trust_remote_code=True,\n",
    "        use_cache=False,\n",
    "         \n",
    "        device_map=\"auto\",\n",
    "        torch_dtype =torch.bfloat16,\n",
    "        attn_implementation=\"flash_attention_2\",\n",
    "        #device_map=\"cuda:0\",\n",
    "    ).to (device)\n",
    "    ref_model.config.use_cache = False\n",
    "\n",
    "tokenizer.encode ('<|thinking|><|/thinking|><|scratchpad|><|/scratchpad|><|reflect|><|/reflect|><|response|><|/response|>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac9d4bb-5e08-46b3-bc34-9d40d0febe4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up default values for the API key, model, and organization\n",
    "generate_GPT_local = partial(\n",
    "    generate_local_model,\n",
    "    model=model, tokenizer=tokenizer,\n",
    ")\n",
    "\n",
    "prompt=f'What is spider silk? Use a {think_start}.'\n",
    "messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": 'You are a materials scientist.',\n",
    "            },\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": prompt,\n",
    "            }\n",
    "        ] \n",
    "\n",
    "res, _ = generate_GPT_local (messages=messages,max_new_tokens=128, #prepend_response=f'<|thinking|>',\n",
    "                            )\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81cf7150-e33b-431d-af44-f19624200e40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "txt='Tell me why hierarchical structures work so well.'+f' Use {think_start}.'\n",
    "\n",
    "output_text, _ =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                           system_prompt='',   #prepend_response=f'{think_start}',\n",
    "                               num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                               temperature=.01,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                               )\n",
    "print (output_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58ec3df-f389-45cf-beb0-a10ee7b18d34",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### Set up PRefLexORDPOTrainer for Phase II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2692414-5912-48ee-a31b-e7a6f3948fa0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "FT_model_name='Graph_PReFLexOR_Phase_II_results'\n",
    "model_current=FT_model_name+'_'\n",
    "\n",
    "max_prompt_length=512\n",
    "max_length=1024\n",
    "\n",
    "class RewardLoggingCallback(TrainerCallback):\n",
    "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
    "        if state.log_history:\n",
    "            try:\n",
    "                print ( f\"Step={state.log_history[-1]['step'] }\", \"rewards/margins=\", state.log_history[-1]['rewards/margins'] ,\n",
    "                      \"loss=\", state.log_history[-1]['loss'],   \"rewards/accuracy=\", state.log_history[-1]['rewards/accuracies'] )# Get the last log entry\n",
    "            except:\n",
    "                print (end='')\n",
    " \n",
    "cfg = DPOConfig(\n",
    "    output_dir=FT_model_name,     # usual HF Trainer args: https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer.args\n",
    "    num_train_epochs=1,                     # number of training epochs\n",
    "    per_device_train_batch_size=1,          # batch size per device during training\n",
    "    gradient_accumulation_steps=2,          # number of steps before performing a backward/update pass\n",
    "    gradient_checkpointing=False,            # use gradient checkpointing to save memory\n",
    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
    "    logging_steps=10,                       # log every .. steps\n",
    "    bf16=True,                              # use bfloat16 precision\n",
    "    #tf32=True,                             # use tf32          \n",
    "    max_grad_norm=0.3,\n",
    "    learning_rate=1e-6,                     # learning rate\n",
    "    warmup_ratio=0,\n",
    "    warmup_steps=0,\n",
    "    #lr_scheduler_type=\"cosine\",\n",
    "    lr_scheduler_type=\"constant\",\n",
    "    max_prompt_length=512,\n",
    "    max_length=2000,\n",
    "    remove_unused_columns=False,\n",
    "    #max_length=1024,\n",
    "    beta=0.5,                               # ORPO beta\n",
    "    save_total_limit=50,                     # args related to saving the model...\n",
    "    save_strategy=\"epoch\",\n",
    "    #push_to_hub=True,                       \n",
    "    hub_private_repo=True,\n",
    "    report_to=['none'],                    # report metrics to Weights & Biases\n",
    "    hub_model_id=f'lamm-mit/{FT_model_name}',\n",
    "    loss_type=\"exo_pair\",                  # Loss type for DPO\n",
    "    label_smoothing=5e-3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f0ffa6-766e-4433-b265-4a1bd1c43181",
   "metadata": {},
   "source": [
    "#### Define new function to generate rejected answer using current model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "479265fe-c875-46cb-8b1f-c984b6ae6327",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_rejected_answer(generate_fn, question, get_from_trained_model, model, tokenizer):\n",
    "    \n",
    "    if get_from_trained_model:\n",
    "    incorrect_answer, _ = generate_local_model(\n",
    "            model=model,\n",
    "            tokenizer=tokenizer,\n",
    "            prompt=f\"{question}\"+f' Use {think_start}.',\n",
    "            system_prompt='You are a materials scientist.', max_new_tokens=1500,\n",
    "        )\n",
    "    else:\n",
    "        prompt = f\"\"\"\n",
    "You are to provide an incorrect answer to the question below.\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Do not include any reasoning or refer back to the question.\n",
    "\n",
    "Just provide the incorrect answer.\n",
    "\"\"\"\n",
    "        messages = [\n",
    "            {\"role\": \"system\", \"content\": 'You are a materials scientist.'},\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ]\n",
    "        incorrect_answer, _ = generate_fn(messages=messages)\n",
    "    return incorrect_answer.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "419cdac3-003f-4f64-ae5d-ba3c116b2b66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Example usage  \n",
    "\n",
    "question, correct_ans, rejected_ans=get_question_and_answers(generate=generate_GPT_MistralRS, model=model,\n",
    "                                                             tokenizer=tokenizer, index=index, topic=None, #'spider silk strength', \n",
    "                             number_nodes_to_get=3, nodes=None, text=None,  \n",
    "                             get_rejected_from_trained_model=True,\n",
    "                                                            )\n",
    "\n",
    "print(\"Question:\")\n",
    "print(question)\n",
    "print(\"\\nCorrect Answer with THINKING:\")\n",
    "print(correct_ans)\n",
    "print(\"\\nRejected Answer:\")\n",
    "print(rejected_ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b140406-05b9-4938-81ac-92964f1a8245",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, concatenate_datasets\n",
    "\n",
    "topics= 50\n",
    "num_questions_per_topic=1\n",
    "num_epochs_per_dataset_generation=1\n",
    "\n",
    "if isinstance(topics, list) and all(isinstance(t, str) for t in topics):\n",
    "    n_steps = len(topics) * num_questions_per_topic*num_epochs_per_dataset_generation\n",
    "else:\n",
    "    n_steps = topics * num_questions_per_topic*num_epochs_per_dataset_generation\n",
    "\n",
    "trainer = PRefLexORDPOTrainer(\n",
    "        model=model,\n",
    "        ref_model=ref_model, # set to none if use LoRA\n",
    "        args=cfg,\n",
    "\n",
    "        train_dataset=None, \n",
    "        tokenizer=tokenizer,\n",
    "        n_steps=n_steps,  # Train for 50 steps before updating dataset\n",
    "        topics=topics,\n",
    "        number_nodes_to_get=3,\n",
    "        n_questions_for_each=num_questions_per_topic,\n",
    "        only_include_wrong_answers=False, \n",
    "        process=process,\n",
    "    generate_dataset=generate_dataset,\n",
    "    generate=generate_GPT_MistralRS, #generate_GPT_OpenAI,\n",
    "    get_rejected_from_trained_model=True,\n",
    "    index=index,\n",
    "\n",
    "    #OPTION 1: Only include answer in loss\n",
    "    dynamic_answer_comparison = True,  \n",
    "    \n",
    "    #OPTION 2: Mask out all sections of thinking, to a degree (percentage indicates how much is randomly masked out)\n",
    "    mask_thinking_tokens = False, \n",
    "    thinking_token_mask_percentage = .2,  # Default to masking 100% of thinking tokens\n",
    "\n",
    "    think_start_token= '<|thinking|>', think_end_token= '<|/thinking|>',\n",
    "    include_thinking_token_in_labels=True,\n",
    "    callbacks=[RewardLoggingCallback()],\n",
    "\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90fb3e5a-7b94-497a-bafe-65735ccddf97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "system_prompt='You are a materials scientist.'\n",
    "\n",
    "# Training loop\n",
    "num_iterations = 50\n",
    "\n",
    "for iteration in range(num_iterations):\n",
    "    print(f\"Starting iteration {iteration + 1}/{num_iterations}\")\n",
    "    \n",
    "    # Train for N steps\n",
    "    trainer.train()\n",
    "        \n",
    "    print (64*\"#\")\n",
    "    # Update dataset\n",
    "    txt='Tell me why hierarchical structures work so well.'#+f' Use {think_start}.'\n",
    "    output_text, _ =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,   #prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text)\n",
    "    print (64*\"-\")\n",
    "    #trainer.update_dataset()\n",
    "    txt='Tell me how the properties of flower petals can be used to write a song.'+f' Use {think_start}.'\n",
    "    output_text, _ =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,   prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text)\n",
    "    print (64*\"-\")\n",
    "    txt='Explain the relationship between materials and music.'+f' Use {think_start}.'\n",
    "    output_text, _ =generate_local_model (model=model, tokenizer=tokenizer,  prompt=txt,\n",
    "                                               system_prompt=system_prompt,   prepend_response=f'{think_start}',\n",
    "                                   num_return_sequences=1,  repetition_penalty=1.0, #top_p=top_p, top_k=top_k,  \n",
    "                                   temperature=.1,max_new_tokens=1024, messages = [], do_sample=True,\n",
    "                                   )\n",
    "    print (output_text) \n",
    "    print (64*\"#\")\n",
    "    \n",
    "    trainer.update_dataset()\n",
    "    \n",
    "    print(f\"Completed iteration {iteration + 1}/{num_iterations}\")\n",
    "\n",
    "    trainer.save_model(f\"./{model_current}\")\n",
    "    model.push_to_hub (f\"lamm-mit/{model_current}\", private=True)\n",
    "    tokenizer.push_to_hub (f\"lamm-mit/{model_current}\", private=True)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bd87c00-8ef1-4150-9f61-414741795eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model(f\"./{model_current}\")\n",
    "model.push_to_hub (f\"lamm-mit/{model_current}\", private=True)\n",
    "tokenizer.push_to_hub (f\"lamm-mit/{model_current}\", private=True)    "
   ]
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "conda-base-py",
   "name": "workbench-notebooks.m123",
   "type": "gcloud",
   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m123"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
